class_docstr: Main TabTransformer model that inherits from AbstractModel.
  
  This model includes the full torch pipeline (TabNet) and the internal Transformer embeddings (TabTransformer).
  This file serves as the connection of all these internal models and architectures to AutoGluon.
  
  TabTransformer uses modifications to the typical Transformer architecture and the pretraining in BERT
  and applies them to the use case of tabular data. Specifically, this makes TabTransformer suitable for unsupervised
  training of Tabular data with a subsequent fine-tuning step on labeled data.
code: def __init__(self, **kwargs):
      try_import_torch()
      super().__init__(**kwargs)
      self._verbosity = None
      self._temp_file_name = "tab_trans_temp.pth"
      self._period_columns_mapping = None
  
imports: import logging
  import os
  import time
  import numpy
  import pandas
  from tqdm import tqdm
  from autogluon.common.features.types import R_OBJECT, S_TEXT_AS_CATEGORY, S_TEXT_NGRAM
  from autogluon.common.utils.try_import import try_import_torch
  from autogluon.core.constants import BINARY, MULTICLASS, REGRESSION
  from autogluon.core.models.abstract.abstract_nn_model import AbstractNeuralNetworkModel
  from autogluon.core.utils.loaders import load_pkl
  from .hyperparameters.parameters import get_default_param
  from .hyperparameters.searchspaces import get_default_searchspace
label_desc: Use image node_id 1 to create a new TabTransformerModel object from inherited base classes: AbstractNeuralNetworkModel with example: obj = TabTransformerModel()